Comparison of statistical tests (Wald, score, likelihood ratio, Rao-Scott) for generalized linear models in survey data, analyzing Type I error and power.
The author discusses the unexpected computational challenges of implementing score tests for generalized linear models in survey statistics.
Explores kernel methods and L1 distances for statistical two-sample testing, comparing their effectiveness in determining if datasets come from the same distribution.
A critique of the Shapiro-Wilk normality test, arguing it's often misused due to the Central Limit Theorem and is rarely the scientifically relevant question.
Explains statistical methods for testing random number generators in R, focusing on hypothesis testing and probability bounds.
Explores Rao-Scott tests for survey data analysis, comparing them to Wald tests and discussing new Satterthwaite-adjusted Wald tests in the survey package.
Explores the mathematical concept of transitive statistical tests and the conditions under which they can be represented by a single real-valued statistic.
